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Open AccessArticle
LSTMConvSR: Joint Long–Short-Range Modeling via LSTM-First–CNN-Next Architecture for Remote Sensing Image Super-Resolution
by
Qiwei Zhu
Qiwei Zhu
,
Guojing Zhang
Guojing Zhang
Xiaoying Wang
Xiaoying Wang
Jianqiang Huang
Jianqiang Huang
1
School of Computer Technology and Application, Qinghai University, Xining 810016, China
2
Intelligent Computing and Application Laboratory of Qinghai Province, Qinghai University, Xining 810016, China
3
School of Computer and Information Science, Qinghai Institute of Technology, Xining 810018, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2745; https://doi.org/10.3390/rs17152745 (registering DOI)
Submission received: 11 July 2025
/
Revised: 5 August 2025
/
Accepted: 6 August 2025
/
Published: 7 August 2025
Abstract
The inability of existing super-resolution methods to jointly model short-range and long-range spatial dependencies in remote sensing imagery limits reconstruction efficacy. To address this, we propose LSTMConvSR, a novel framework inspired by top-down neural attention mechanisms. Our approach pioneers an LSTM-first–CNN-next architecture. First, an LSTM-based global modeling stage efficiently captures long-range dependencies via downsampling and spatial attention, achieving 80.3% lower FLOPs and 11× faster speed. Second, a CNN-based local refinement stage, guided by the LSTM’s attention maps, enhances details in critical regions. Third, a top-down fusion stage dynamically integrates global context and local features to generate the output. Extensive experiments on Potsdam, UAVid, and RSSCN7 benchmarks demonstrate state-of-the-art performance, achieving 33.94 dB PSNR on Potsdam with 2.4× faster inference than MambaIRv2.
Share and Cite
MDPI and ACS Style
Zhu, Q.; Zhang, G.; Wang, X.; Huang, J.
LSTMConvSR: Joint Long–Short-Range Modeling via LSTM-First–CNN-Next Architecture for Remote Sensing Image Super-Resolution. Remote Sens. 2025, 17, 2745.
https://doi.org/10.3390/rs17152745
AMA Style
Zhu Q, Zhang G, Wang X, Huang J.
LSTMConvSR: Joint Long–Short-Range Modeling via LSTM-First–CNN-Next Architecture for Remote Sensing Image Super-Resolution. Remote Sensing. 2025; 17(15):2745.
https://doi.org/10.3390/rs17152745
Chicago/Turabian Style
Zhu, Qiwei, Guojing Zhang, Xiaoying Wang, and Jianqiang Huang.
2025. "LSTMConvSR: Joint Long–Short-Range Modeling via LSTM-First–CNN-Next Architecture for Remote Sensing Image Super-Resolution" Remote Sensing 17, no. 15: 2745.
https://doi.org/10.3390/rs17152745
APA Style
Zhu, Q., Zhang, G., Wang, X., & Huang, J.
(2025). LSTMConvSR: Joint Long–Short-Range Modeling via LSTM-First–CNN-Next Architecture for Remote Sensing Image Super-Resolution. Remote Sensing, 17(15), 2745.
https://doi.org/10.3390/rs17152745
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